During the planning phase, you should reflect on how you will collect, structure, organise, store, and delete data. This is essential for ensuring that you manage your data effectively and for protecting the data and informants involved. 

It may be beneficial to document your decisions in a log book or similar format. 

The type of data you collect will depend on your academic discipline, the subject you are investigating, and the methods you are using. Common categorisations of data include: 

Primary and secondary data 

• Primary data refers to information collected directly from the source, such as through observations, surveys, interviews, or experiments. 
• Conversely, secondary data consists of information gathered in a context other than the current study. This data is often processed and organised in a meaningful way and can be sourced from books, articles, reports, statistics, or public databases. 

Qualitative and quantitative data

• Qualitative data describes the characteristics of a person, object, or situation. This can include feelings, attitudes, opinions, experiences, or descriptions. Qualitative data is often not quantified in numerical terms but can be analysed using text, images, or audio. When collecting qualitative data, such as interviews, you may need to use a voice recorder. At AU Library, we lend voice recorders to students.  
• Quantitative data, on the other hand, describes measurable characteristics, such as quantities, measurements or data given in measurable terms. Examples of quantitative data include counts, lengths, weights, temperatures, times, or other measurable attributes. 

Finding data collected by others

It is possible to find open data that you can use for your assignments. These are often published by public institutions, organisations, or researchers. 

You can find data in various repositories: 

• Institutional repositories contain data from researchers at their respective universities. 
• Field-specific repositories include data from one or a few related fields but may be open to all. 
• General repositories cater to everyone, regardless of institution, discipline, or data type. 

You can search for repositories at Re3data.org, a database of available data repositories. Here, you can filter repositories based on criteria relevant to your assignment. 

A widely used general repository is Zenodo. 

Once you have collected data, it is important to think about how you will organise it. This includes how you will name and structure your folders; making it easier to navigate your dataset. 

Data Formats 

Data is stored in files, which come in various formats. A good practice is to save your files in formats that can be opened by anyone, regardless of whether they have access to specific software. This approach not only benefits others but also serves your future needs. 

Examples of open-access file formats include plain text files (.txt), PNG files (.png), and CSV files (.csv). In contrast, proprietary formats, such as Word documents (.doc or .docx) and Excel spreadsheets (.xlsx), may limit accessibility. 

Aarhus University provides Microsoft 365 to all students. There are also alternatives to commercial software. 

File Structure and Naming Conventions 

Keep your files organised by using meaningful names, logical folder structures, and by using a consistent file naming system. 

By maintaining consistency in your file naming, you increase the chances of easily locating the correct file when you or others need it. 

Computers typically sort files in File Explorer (PC) or Finder (Mac) either alphabetically or numerically. Therefore, it’s advisable to place the most important information at the beginning of the file name. 

Examples of naming conventions: 

• [initials]_[method]_[topic]_[YYYYMMDD]_[version]_[xxx] 
• [project#]_[method]_[version]_[YYYYMMDD]_[xxx] 
• [filetype]_[initials]_[date]_[xxx] 
• [YYYYMMDD]_[chapter name]_[document name]_[version] 

Documenting data means providing sufficient information about your dataset. This ensures that you or others can understand, interpret, and utilize the data at a later point in time. 

Depending on the context and the type of research you are conducting, you may include the following information: 

• Details about the equipment used, such as brand and model, settings, and calibration information
• Information about the methods or theories applied
• Text for questionnaires, interview templates, discussion guides, etc.
• Information about who collected the data and when
• Key features of the methodology, such as sampling techniques, whether the experiment was blinded, and how participants were identified and grouped 
• Legal and ethical agreements regarding the data, such as consent forms, data licenses, and approval documents
• References to any secondary data you have utilized
• Details about file formats
• Information about the software used to generate or process data, including version numbers and platforms

Documentation can be recorded in various formats, including: 

• A README file: A structured text file where you describe your dataset and explain how it was collected and analysed
• A log book: A place to record your observations, interpretations, and empirical data
• A code book: A document where you define the variables you are using, their relationships, units of measurement, and how you note any gaps in the dataset 

The above section is based on the data management section from the University of Copenhagen’s learning resources for digital literacy, 2023. CC-BY-NC-SA. 

When you collect data, you must also decide where to store it. There are various options available, such as: 

• Hard drives on computers 
• External hard drives 
• USB flash drives 
• Servers 
• Cloud solutions 

It is your responsibility to ensure that the data you are working with is stored securely to prevent data loss or unauthorized access. Depending on the types of data you are handling and how they are classified, different levels of security may be required for storage. 

Aarhus University offers OneDrive as a secure location for data storage. You can store all types of data there, as long as the data is pseudonymised or anonymised. 

Read more about data storage on Aarhus University’s website about processing personal data. 

Backup 

It is also important to remember to back up your data or ensure that the storage infrastructure you are using does so for you. For instance, if you store your data on OneDrive, backups of files and folders are created automatically, allowing you to access them even if you lose your computer. Conversely, if your data is only stored on your computer’s hard drive and it is lost, the data will be irretrievable. 

A golden rule for backups is known as the 3-2-1 rule. This principle states that you should keep 3 copies of your data on 2 different media, with 1 of those media being a cloud solution like OneDrive. 

If your dataset includes works created by others, such as newspaper articles or photographs, these works may be protected by copyright. 

If a work is protected by copyright and there is no agreement or license granting you permission to use it, you must obtain permission to use the work yourself. 

As a student, you may use data that is not protected by copyright, or data that has been published in an open repository. If a creator has released a work under a Creative Commons license, you can use that work in accordance with the terms of the CC license. 

Read more about copyright on the library's copyright page. 

There are many types of data that may contain personal identifiable information, such as interviews, surveys, images, and more. 

Personal identifiable information refers to information that can be used to identify a specific individual. 

If your data contains personal identifiable information that can be used to identify living individuals (or individuals who have passed away within the last 10 years), you must comply with the EU’s GDPR regulations. 

Aarhus University has clear guidelines regarding the handling of personal identifiable information in relation to assignments, which you are required to follow. 

Different types of personal data require varying levels of security for storage. Read more about Aarhus University’s classification of data. 

Consent forms 

It is legally required and a central part of good academic practice to obtain informed consent when collecting data about identifiable individuals (e.g., interview materials, images, and observations). You must be able to document this consent in writing. Aarhus University provides a template for a consent form that you can use. 

Pseudonymisation and anonymisation 

You can choose to pseudonymise or anonymise your data. 

When you pseudonymise your data, you create a confidential record that allows you to identify individuals again, for instance, by assigning each person a numerical code or a pseudonym. It is not sufficient to simply assign a numerical code or pseudonym to each individual; you must also remove any identifying characteristics from the dataset that could allow individuals to be identified without your record. 

When you anonymise data, you delete all information that could be used to identify individuals. This means you do not create a record that allows you to identify the individuals again. Proper anonymisation is therefore irreversible. 

Once data has been irreversibly anonymised, it is no longer considered personal data and is not subject to data protection laws. 

In some cases, anonymising your data may therefore be advantageous. 

Aarhus University offers various tips on how to pseudonymise and anonymise data correctly. 

[Translate to English:]

Der findes mange typer data, som kan indeholde personhenførbare oplysninger, f.eks. interviews, spørgeskemaer, billeder m.v.  

Personhenførbare oplysninger er oplysninger, der kan bruges til at identificere en bestemt person. 

Hvis dine data indeholder personhenførbare oplysninger, der kan bruges til at identificere nulevende personer (eller personer som har været døde i mindre end 10 år), skal du forholde dig til EU’s GDPR-forordning.   

Aarhus Universitet har nogle klare retningslinjer vedrørende behandling af personhenførbare oplysninger i forbindelse med opgaver, som du skal følge. 

Forskellige typer af persondata, kræver forskellige niveauer af sikkerhed i opbevaringen. Læs mere om Aarhus Universitets’ klassifikation af persondata. 

Samtykkeerklæringer 

Det er lovpligtigt og en central del af god akademisk praksis at sikre sig informeret samtykke, når du indsamler data om identificerbare personer (f.eks. interviewmateriale, billeder og observationer). Du skal kunne dokumentere samtykket på skrift. Aarhus Universitet har en skabelon til samtykkeerklæringer, som du kan bruge. 

Pseudonymisering og anonymisering 

Du kan vælge at pseudonymisere eller anonymisere dine data. 

Når du pseudonymiserer dine data, laver du en fortrolig fortegnelse, som tillader dig at identificere personerne igen, f.eks. ved at give hver person en talkode eller et pseudonym. Det er ikke tilstrækkeligt, at du blot giver hver person en talkode eller et pseudonym. Du skal også fjerne de kendetegn i datasættet, som gør det muligt at identificere personer uden din fortegnelse. 

Når du anonymiserer data, sletter du alle oplysninger, som kan bruges til at identificere personer. Dermed laver du ikke en fortegnelse, som tillader dig at identificere personerne igen. Korrekt anonymisering er derfor uigenkaldelig.  

Når du uigenkaldeligt har anonymiseret dine data, er det ikke længere persondata og er ikke underlagt persondatalovgivning. 

I nogle tilfælde kan det derfor være en fordel at anonymisere data.  

Aarhus Universitet har forskellige tips til, hvordan du pseudonymiserer og anonymiserer korrekt.