The aim in using a standard format is to eliminate confusion (especially around the different way dates are represented in the USA compared to Europe, for example) and to make your files more easily searchable and sortable. The specific format is year first, followed by month then day. Each of these is separated by a hyphen, with numbers less than 10 preceded by a zero. By using this format, you will be able to sort your digital files more easily into chronological order.
For example: 3 June 2022 = 2022-06-03
Big tip: There are no spaces!
Your file names may end up looking something like this:
2020-12-01_participants-list.xml
2022-06-03_interview.doc
2022-07-03_analysis.r
Remember that computer file systems have a specific way of sorting files. If your file names all start with an ISO date, they will automatically sort from oldest to newest using the default alphabetical sort. Any other date-based naming convention would create different results.
When applying dates to digital file names, the International Organization for Standardization (ISO) date format is recommended. This is a standard way to communicate a date that is understood globally, both by humans and computers. It is also known as .
This chapter covers skills and knowledge required for living, learning and working with digital information, media and data.
Topics include:
Information literacy skills and knowledge: How to find, evaluate, manage and disseminate information.
Media literacy skills and knowledge: How to develop skills for the creation and consumption of, and participation in, digital media.
Data literacy skills and knowledge: will support efficient, effective and ethical creation, maintenance, interpretation, dissemination and preservation of data in both personal and professional capacities.
• a known or assumed fact that is used as the basis for calculation or reasonings
• a collection of facts used as information or for reference
• “quantities, characters, or symbols” in the form of electrical signals that can be used, stored, or transmitted with computer equipment
The first two are general definitions that apply equally to the real and digital worlds, but we are most interested in the digital data that is represented by the third definition.
To complicate things further, data can be structured or unstructured. Data that is ready for analysis is structured, so let’s talk about that first. More information about unstructured data has been included in part three of this topic.
This chapter will give you a general understanding of what is meant by the term “data” and some context for thinking about your own use of data.
There are two basic types of structured data: qualitative and quantitative. In this article, we will discuss both data types, some of the different forms they might take, and give some examples of ways in which that data might be analysed.
Let’s discuss qualitative data in a bit more detail. Qualitative data is data that refers to the quality of something - it is generally not an objectively defined measurement but an opinion or generalisation which is often text based. There are generally three subtypes: binary, nominal, ordinal.
• Binary data records something that has two mutually exclusive traits (ie. true or false).
• Nominal data describes objects using categories and/or labels (ie. colours of cars). It can also be used to give counts of a particular attribute.
• Ordinal data records the order or ranking of something (ie. Education systems have ordered primary, secondary, and tertiary levels, however the difference between each level cannot be quantified specifically).
Now let’s talk more about quantitative data. Quantitative data is based on an objective measurement of something and is numerical in nature. It can be easily used for mathematical and statistical calculations and those results then used for further analysis. Many scientific studies make use of quantitative data. Generally, the sorts of data collected are either measurements or counts.
• Measurements are taken according to a specified scale such as length, weight, and currency. Interval scales and ratio scales describe the different measurement types in more detail but we don’t need to delve into that much detail for this example.
• Counts are the frequency of which something occurred (7 lightning strikes) or answers how many of something (5 frogs).
A good way to remember the difference between the two types of data is that qualitative data is indicative of the qualities and characteristics of something, whereas quantitative data is all about recording the quantity or measurements of something!
Each of the two data types discussed above are generally considered in terms of structured data. The main thing to keep in mind with structured data is that it has already been sorted into a predefined format and there should only be one subtype of one data type recorded in each column of the dataset. Below is an excerpt dataset example of structured data, and you will notice that there are both qualitative and quantitative data.
33
5.2
4.1
1.5
0.1
I. setosa
34
5.5
4.2
1.4
0.2
I. setosa
99
5.1
2.5
3.0
1.1
I. versicolor
100
5.7
2.8
4.1
1.3
I. versicolor
101
6.3
3.3
6.0
2.5
I. virginica
Each row of the dataset is the information collected about one iris flower. Each number in the first column “Dataset Order” is ordinal qualitative data: the number identifies the sample in the dataset, no calculations can be based on that number. Columns two to four are all quantitative measurements (in centimetres): parts of sampled iris flowers that could potentially be used in mathematical calculations to compare samples. The last column is nominal qualitative data: the “Species Name”.
The example above specifically shows tabular data as it is human readable or easy for a person to visualise. However, there are ways to structure data without putting it into a tabular form. Examples of this include JSON files and markup languages which structure data in a machine readable form. It can be challenging to decipher the structure of such data without the aid of a computer program to parse it into a human readable form. For example, remote sensing data is generated by machines and includes things like seismic and atmospheric measurements, digital surveillance photos and videos, and satellite imagery. This output is structured and readable for machines, but can be difficult for a human to decipher. It often requires a specific program to output the data in a human readable form.
Unstructured data has not been sorted or formatted and may even be in a narrative form. The primary source can take many different forms including text and media.
Text data could be a journal entry of someone’s observation of something, an opinion, an experience, a narrative. Media that may take the form of photos, videos, or sound files.
The data extracted from the primary source needs to be transformed into a coherent structure to allow for computational analysis. The extracted data could be comprised of counts specific words in a particular collection of texts or digital surveillance photos that contain a particular object. Whatever the data is, it must be organised, or structured, so that individual attributes are grouped with similar, comparable attributes. Whether the dataset needs to be structured to be machine- or human-readable depends on the project and the types of analysis to be performed on that dataset.
defines data as a “unit of information” about a person or object that could be a fact, statistic, or other item of information. And according to the Oxford English Dictionary entry, “data” has multiple definitions. Three of the separate but interlinked meanings are particularly helpful in our context of discussing digital data. The three definitions are:
Searching for information can feel a little like drinking from the proverbial firehose. While relying on search algorithms to interpret your quick and dirty search will usually do the trick for lower-stakes day-to-day searches, when it comes to in-depth searching for professional or academic purposes, you need to do a little bit more work constructing your search to track down the resources you need.
A search for information starts with a need for information. Sometimes it helps to have this need phrased in the form of a question if it isn’t already. This can help you identify the concepts within your need, and it’s the concepts that drive your search term selection.
Look at this research question:
How does diet affect the symptoms of people with depression?
What are the concepts the above question touches on? Which of them are actually useful in a search?
“Diet” and “depression” are the two major concepts that jump out immediately. They are the crucial elements of this question, and someone searching on this question would definitely be interested in papers that mentioned both of these concepts.
The concept of “affect” or something being affected by something else is certainly important to how you end up answering this question but is it actually helpful for a search aimed at gathering literature related to the question? For example, if you had this question would you still be interested in papers that discussed changes in diet for people with depression, even if the paper didn’t use the word "affect" in it? And wouldn’t using “improve” also lend bias to your search in that requiring it to appear in a paper would rule out papers that found dietary change made no difference? With this in mind, “affect” is less important and possibly detracts from the quality of the search.
Finally, “symptoms” and “people” are both too general and vague to be useful as search terms. Adding “symptoms” to the search may mean you miss papers where the word “symptoms” isn’t used; also, a search for “diet” and “depression” implies some relationship between the two, so the search doesn’t need to be so specific about what that relationship is (i.e. that it relates to symptoms).
Key point:
From your research question, consider which concepts are the most important and practical to search with.
With a database search, sometimes less is more!
Once we have identified the concepts that are useful for our search, the next step is to generate some search terms for each. While each concept is a useful search term on its own, we need to consider other ways the concepts could be expressed and other words that are associated with them in order to capture as much of the relevant literature as possible.
Continuing with our example, we can generate these additional search terms by thinking about them ourselves and by looking at the words used in the relevant resources we find. Often it helps to put these into a table or some other kind of search planning document in order to keep track of what you’re finding.
Here’s an example of such a table for tracking purposes; note that below the concepts appear as search terms along with a couple of additional search terms related to each:
Concepts ->
Diet concept
Depression concept
Search terms ->
Diet
Depression
Search terms ->
Nutrition
Depressed
Search terms ->
Food
Mood
Key points
Your concepts are a starting point for search terms that help you find relevant records in a database. Possible search terms include synonyms, antonyms and other related terms for each concept.
Keep track of your search terms using a search planner.
Once you have a set of search terms generated, it’s time to start putting them to work. This makes choosing which database(s) to search in is the next step.
Depending on the nature of your question you may be looking at discipline-specific databases or multi-disciplinary databases. Generally, discipline-specific databases have very good depth of coverage on a narrow field whereas multi-disciplinary are more broad as they attempt to cover many fields. It may also be important to consider if the database focuses on a particular geographical, historical or other area, and how that fits with your search in terms of what you could expect to find within the database.
Key point
You will need to understand a little bit about the database in order decide whether to use it for your search.
Examine the database’s Help or About sections, or the database information or guides provided by the library that is faciliating access to the database.
While some databases can infer the relationship between your search terms, it is best to be as direct as possible and tell the database what you want. A fundamental method of doing this is to use the words AND, OR and NOT. These are common search operators (AKA Boolean operators) which tell the database how to treat the search terms within a search:
Putting AND between search terms tells the database that you want to see records that feature both of the terms; in other words, if only one term appears in a record, you don’t want to see it. Using AND narrows your search by putting extra requirements on it.
Putting OR between search terms tells the database you’d be happy to see records that feature either of the search terms; in other words, if either term appears in a record, you want to see it, even if the other term(s) don’t appear. Using OR broadens your search as it gives the database more options for records to show you.
Putting NOT before a search term tells the database that you don’t want to see any records containing that term. Using NOT narrows your search by removing irrelevant results. However, NOT should be used very cautiously as it could remove relevant results that also feature (perhaps inadvertently or as a comparison) the search term you have used NOT with. While it’s good to know about NOT, it’s unlikely you’ll use it very often in your searches.
It can also be helpful to use parentheses to group together your search terms. This isn’t always necessary but is a useful way to control the relationship between the search terms so that the database doesn’t do something unexpected.
Continuing with our diet and depression question, this example search uses AND, OR and parentheses to tell the database what you’re looking for:
(diet OR nutrition OR food) AND (depression OR depressed OR mood)
In the above, we’re asking the database to show us records that feature any one or more of the search terms within each set of parentheses (or each concept group). That is, if a record has any one of the search terms from the diet concept group, as well as any one of the search terms from the depression group, the database will show it to us. If a database record features a term from one group but not another, it will not be shown in the search results for the above search.
Key point
When needed, using AND, OR, NOT or parentheses to control the relationship between your search terms is a useful way to tell the database exactly what you’re looking for.
Field codes
Subject headings
Trunctation, wildcards, phrase searching
Proximity searching
Search filters
Citation searching
While the concept of fake news isn’t new, it has become more ubiquitous with the popularity of social media and the increasing reliance on social media in everyday life. Social media gives anyone the power to share information, whether true or false, in many different forms. You may come across fake news in the form of legitimate looking news articles, YouTube and social media videos, memes, infographics, viral content, radio and TV broadcasts, by word of mouth, and more.
This article will cover:
Spotting misinformation and fake news
Lateral reading
Filter bubbles
Deep fakes
Fake news involves hoaxes, opinions, scams, or misleading or false information presented as legitimate news stories. It is capable of disrupting elections, economies, social movements, reputations, geo-political conflicts and more. While some fake news may appear fake, others are increasingly more sophisticated.
It’s easy to fall into the trap of fake news, particularly when a piece of media confirms our existing beliefs and ideas. This is known as confirmation bias. However, despite how much we may want something to be true to support our beliefs, it is important to check our biases and critically analyse the information we find online.
There are several common features of fake news that may immediately flag a piece of information as suspicious. These include:
Provocative or misleading headlines
Clickbait
Propaganda or opinion pieces
Unusual URLs
Distorted dates
Fake authors
However, some articles may not immediately strike you as suspicious. When reading news or information online, no matter how sophisticated, you should always check for the following:
Is the author qualified to be writing on this topic? Do they possess relevant qualifications or work experience?
If the author is an organisation, is it recognised and trustworthy?
Is there an ulterior motive for presenting the information, particularly a financial or political one?
Why was the content created?
Is its goal to inform (presenting an objective and evidence-based account of the topic)?
Is its goal to persuade (presenting a biased or one-sided account of the topic)?
Is the information trying to sell you a product?
If claims are being made, is there evidence to support them, either as references or links to further information?
If the author is using references, are they from authors who are qualified to write on the topic?
Can you find corroboration for the points being made elsewhere on the internet?
Are the quotes or examples used only part of what was said? Have they been taken out of context?
The best way to establish the credibility of information is through a process called lateral reading. The process of lateral reading involves reading across sources, checking information as it’s presented to you. This contrasts from simply reading a webpage's 'About Us' page and taking it at face value, to also searching the rest of the web for more information about the author or organisation who created the information, the date it was posted, what others have to say about the topic and more. As an evaluation method, we recommend the CRAAP method, which checks for Currency, Relevance, Authority, Accuracy and Purpose.
Filter bubbles are echo chambers of like-minded views, curated through the personalisation of online services (including news clicks, search behaviour, and social media algorithms). Filter bubbles affect the flow of information that you receive, reducing or preventing exposure to information that may challenge our world view.
Filter bubbles play a large part in online communities where false news can easily spread. Additionally, as you are not getting an accurate view on the world, you may be surprised by certain world events, such as the results of Brexit and the 2016 US elections.
Filter bubbles are therefore the result of both social media algorithms and our own behaviour. Recognising that you are in a filter bubble is the first step to breaking free from it. The next steps are finding ways to actively re-train the algorithm to diversify your feed and the information you receive.
Use ad-blocking extensions
Use incognito browsing
Delete or block browser cookies
Interact with a wider range of people
Interact with content outside your comfort zone
Follow differing perspectives
Deep fakes are a sophisticated form of fake news, where fictional videos or images are created featuring well-known or made-up people doing or saying things that are not real. This advanced form of manipulation is created using artificial intelligence and machine learning.
Search for surrounding media or do a reverse image search of a video screenshot
Unnatural eye movement or facial expressions
A lack of emotion
Awkward-looking body or posture
Poor lip-syncing
Errors in fine details, such as hair or teeth
from Deakin University Library
Someone may have also done the work for you! Media literacy sites such as and check the authenticity of news and viral content.
These echo chambers are formed both organically and intentionally throughout your journey through the internet. Everything you do leaves a trace, from interactions on social media to clicking ads and utilising search engines. Web algorithms eat these up, forming highly personalised profiles based on your interests and behaviour. Both and Google develop ad profiles based on what they think you may like, and the more we interact with certain people and their content, the more they’ll be present in your feeds. Further, by actively blocking, muting or unfollowing what we don’t like or agree with, we actively narrow our exposure to certain information and conflicting views.
Turn off personalised ads on , and
This section needs further development.