Module 7 – Data Literacy

In this module we will discuss data literacy.

In our previous module

We discussed publishers and funders, and the things that are important to them. Were there any you were interested in?

One important aspect that can prepare you for working for those publishers and funders is having a solid grasp of data literacy, both how to define it and use it. Data literacy can be difficult to define, and even many professional researchers and organizations have slight variations on their definition.

For our purposes though, we will be using this definition of Data Literacy as outlined in this 2015 paper by Ridsdale C., et al.: “Data literacy is the ability to collect, manage, evaluate, and apply data, in a critical manner.”

Let’s break this down:

Literacy for most people means, being able to read and write, and you’re here reading this tutorial and writing down notes, so you might be wondering what else there is to data literacy. Over the years, the definition of literacy has become more complex as our world becomes more complex. Literacy is not just reading and writing, but also being able to absorb that reading and writing, think about it critically and use it in new and unique ways. Examples of literacy are: writing an essay about gender in Shakespeare or creating a scientific hypothesis from other science experiments that have been read about. It means wielding information creatively.

Extrapolating this to data means that we understand data literacy as a process that involves first reading the data, then understanding more deeply and creating connections with the real world about the data, and then using the data creatively (making graphs, infographics, metadata) to expand on a topic or add to the world.

While the concept of data literacy might seem overwhelming, it is a part of science. And like all sciences it is made up of skills that are practicable, just like any other!

Let’s go over what each part of the definition of data literacy means, in simple terms:

Collect – For this you would be able to critically examine sources of data for trustworthiness (see our Module on Citation Culture) and examine data for errors (see our Module on Dirty Data).

Manage – This means you can create metadata (see our Module on metadata), clean dirty data (see our Module on Dirty Data), and practice active data management.

Evaluate – This means you would be able to know the right data tool for the job, or the right way to analyze data, identify practical solutions to data problems in the ocean sector, and can develop and implement a plan for your data. (See our Module for Data Management Plans)

Apply – This means you can understand and can apply citations methods and licensing for data, are aware of high-level data issues and challenges in your field and know how to follow up on your previous data decisions and decide if they were the right ones.

As you work through this course we’ll touch on various modules that will help to grow each component of your data literacy skills.

These terms from: Ridsdale, Chantel & Rothwell, James & Smit, Mike & Bliemel, Michael & Irvine, Dean & Kelley, Dan & Matwin, Stan & Wuetherick, Brad & Ali-Hassan, Hossam. (2015). Strategies and Best Practices for Data Literacy Education Knowledge Synthesis Report. 10.13140/RG.2.1.1922.5044.

Before you go! Things to consider for the next module:

Do you already feel data literate? Why do you think data literacy is important? Which of the four parts of data literacy (Collect, Manage, Evaluate or Apply) are you most interested in learning more about?