Civic Data Education Series
  • Civic Data Education Series
    • About
    • License
    • Instructor Guide
    • Feedback
  • Segment 1: Introducing Civic Data
    • Module 1.1: Introduction to Open Civic Data
    • Module 1.2: The Lifecycle of Open Civic Data
    • Module 1.3: A Critical Approach to Open Civic Data
  • Segment 2: Understanding Civic Data Ecosystems
    • Module 2.1: Identifying Civic Data Intermediaries
    • Module 2.2: Introduction to Civic Data Ecosystems
      • Module 2.2 Activities
    • Module 2.3: Assessing the Civic Data Needs of Communities
  • Segment 3: Preparing Libraries for Sharing their Data
    • Module 3.1: Selecting and Sharing Open Data
      • Module 3.1 Activities
    • Module 3.2: Protecting Privacy
    • Module 3.3: Metadata for Open Civic Data
    • Module 3.4: Data Documentation for Open Civic Data
      • Modules 3.3 & 3.4 Activities
    • Module 3.5: File Formats for Open Civic Data
  • Segment 4: Community Engagement through Civic Data
    • Module 4.1: Defining Data Visualization
    • Module 4.2: Telling Stories with Data
    • Module 4.3: Outreach on Open Civic Data
  • Segment 5: Using Civic Data
    • Module 5.1: Asking Data Driven Questions
    • Module 5.2: Answering Data Driven Questions Computationally
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  • Instructional Materials
  • Video
  • Scripts and Slide Deck
  • Activity
  • Resources
  1. Segment 5: Using Civic Data

Module 5.1: Asking Data Driven Questions

PreviousSegment 5: Using Civic DataNextModule 5.2: Answering Data Driven Questions Computationally

Last updated 2 years ago

Introduction

As library workers, we may want to draw insights from open civic data data through computational methods. To do this, we need to develop a question that our data can answer. In this module, we explore framing data driven questions.

Guiding Questions

  • How do we frame a research question that we can answer computationally?

Vignette

The librarian wants to draw insights from the library WiFi usage data. They begin developing a question to ask of the data: “Is WiFi being effectively used by library patrons?” But they realize that they need to build greater specificity to the question, defining what "effective" means in the context of this analysis. They decide that one measure of service effectiveness is the number of patrons who use the Wifi. They reframe their question to be a more data driven one, asking: "Are WiFi services effective, measured by achieving the goal of annual WiFi sessions being greater than or equal to 25% of annual patron counts?”

Instructional Materials

Video

Video Files

Scripts and Slide Deck

Script and Slide Deck Files

Activity

Overview:

This exercise can be done individually or in pairs.

Supplies: An Internet-connected device and paper and a pen/pencil for notetaking

Time: 20 minutes

Activity:

  1. Select a dataset

Choose a dataset that is published on the Western Pennsylvania Regional Data Center or a data portal that is local to your region.

2. Interrogate the data

Apply these questions to your dataset and the metadata available on the data portal. Why was the data collected? Who collected the data? What tools were used to collect the data? When was the data collected? What might have been the motivations or perspectives of the individuals and/or groups collecting the data? What data wasn’t collected (or, at least, is not available to us)?

3. Share-Out

Data have context. Share what you learned about the context of your data! Note what you were unable to address, inferred, or found a clear answer to.

Activity Files

Resources

Look at this data activity from data basic:

https://databasic.io/en/culture/ask-questions
The project explores the challenges associated with implementing data science within diverse library environments by examining two specific perspectives framed as ‘the skills gap,’ i.e. where librarians are perceived to lack the technical skills to be effective in a data-rich research environment; and ‘the management gap,’ i.e. the ability of library managers to understand and value the benefits of in-house data science skills and to provide organizational and managerial support. Citation: Burton, Matt and Lyon, Liz and Erdmann, Chris and Tijerina, Bonnie (2018). Shifting to Data Savvy: The Future of Data Science In Libraries. Project Report. University of Pittsburgh, Pittsburgh, PA.
https://d-scholarship.pitt.edu/33891/
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Module5.1_UncaptionedRecording.mp4
Module 5.1 Uncaptioned Video: Asking Data Driven Questions MP4 File
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Module5.1_Script.docx
Module 5.1 Script: Asking Data Driven Questions Word Document File
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Module5.1_Script.pdf
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Module 5.1 Script: Asking Data Driven Questions PDF File
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Module 5.1 Slides.pptx
Module 5.1 Slides: Asking Data Driven Questions PowerPoint File
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Module 5.1 Slides.pdf
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Module 5.1 Slides: Asking Data Driven Questions PDF File
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Module5.1_Activity.docx
Module 5.1 Activity File: Asking Data Driven Questions Word Document File
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Module5.1_Activity.pdf
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Module 5.1 Activity File: Asking Data Driven Questions PDF File