# Module 5.1: Asking Data Driven Questions

## 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

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#### Video Files

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**Module 5.1 Uncaptioned Video:** *Asking Data Driven Questions* \
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### Scripts and Slide Deck

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#### Script and Slide Deck Files

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## Activity

**Overview**:&#x20;

Look at this data activity from data basic: <https://databasic.io/en/culture/ask-questions>

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&#x20;

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&#x20;

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

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## Resources

{% embed url="<http://d-scholarship.pitt.edu/33891/1/Shifting%20to%20Data%20Savvy.pdf>" %}
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/>
{% endembed %}

{% embed url="<https://vimeo.com/236119180>" %}


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