2. Collecting and using data

The types of data collected for a study are primarily determined by the research questions. Data may consist of, for example, images, interviews, statistical datasets, audio recordings, or numerical measurements. In addition, the nature of the data influences how it can be generated and processed.

For instance, interview data typically involve personal data, which means that particular care must be taken when selecting devices and software for data collection and handling. Conversely, data produced by measurement instruments may be large in volume, placing specific demands on storage capacity and data-sharing solutions. It is therefore important to consider in advance which file formats and software are most appropriate for different types of research data and research settings. These choices are also shaped by discipline-specific practices.

In the context of data management, data quality primarily refers to ensuring that data are not accidentally modified, damaged, or lost. Researchers must identify situations and processing steps where the content of the data could be unintentionally altered. Such risks may arise, for example, during file format conversions or when transcribing interviews. Ensuring data quality is therefore closely linked to effective risk management, supported by systematic backup and versioning.

Assessing the quality of data collection is inherently connected to research methods, but it also relates to data management practices. Measures that support quality during data collection include calibrating instruments, taking repeated measurements, and using standardised methods and protocols. In addition, thorough and accurate documentation of the data collection process is a key indicator of high-quality data.

Research data can be classified based on how they are obtained. Data may be collected specifically for a given study (e.g. samples, surveys, measurements, or imaging), drawn from pre-existing sources and reused (e.g. text corpora, image repositories, biobanks, or registers), or produced during the research process (e.g. data generated through analysis or software development).

The origin of the data is important because it determines what can be done with the data and who has the authority to make those decisions. When researchers collect data themselves, they can define the terms of use within the limits set by legislation and contractual agreements. When using pre-existing data, however, the researcher must carefully consider the applicable terms of use. For open-access data, these are typically defined through licences, such as Creative Commons (CC) licences. These terms are discussed in more detail in Chapter 5. 

Existing data can also be reused when collecting research materials. Data reuse refers to using already available data for new purposes, such as research, teaching, studying, or commercial applications. Such data can be found in data repositories and search services (see, for example, the sections Research data servicesand Reuse of open research data in the UEF Open science website). 

Reusing data can save both time and resources, as it reduces the need to collect data from scratch. This option is worth considering whenever suitable data are available.

When reusing pre-existing data, it is essential to cite them in the same way as books or scientific articles. A proper data citation should include the following elements:

• creator 
• title 
• host institution (repository or archive) 
• publication date or time 
• persistent identifier.
  

Additional useful elements may include: 

• version 
• resource type 
• license 
• ORCID (persistent identifier for researchers) 
• possible embargo information.
  

Data repositories and archives often provide specific guidelines for citing datasets. In addition, publishers may have their own requirements for referencing data in academic publications.

Although commonly associated only with research involving personal data, research ethics is relevant to all scientific research. Of course, if the research material contains personal data or information, e.g. on endangered animals, national defence or trade secrets (sensitive/confidential information), research ethics and legislation must be borne in mind from the very beginning of the research and its design. 

It is important that the researcher identifies any ethical, contractual, and juridical aspects and restrictions related to the research data and addresses them throughout the data management process. It is primarily the responsibility of each researcher to implement good scientific practice in their own work, but they are not alone. The responsibility is carried by the entire scientific community, including research teams and their responsible researchers, as well as research organisations and their management.  

In the context of research ethics, we often talk about research integrity. It is based on the principles of reliability, integrity, respect, and responsibility. Research integrity guides our actions at the different stages of research. In Finland, the Finnish National Board on Research Integrity (TENK) provides guidance on matters concerning research ethics in its guidelines for research integrity, which state the following about good research practices in data management: All partners 

• agree in advance about the ownership of the research data and about the rights to its use, its processing, storage and possible reuse 
• if necessary, revisit the agreements later during the course of the research, for potential amendments 
• comply with current data protection legislation and obligations related to non-disclosure, confidentiality and secrecy 
• promote to openness and further use of the data to the extent possible. 

Personal data can be used for scientific research when the use is appropriate, planned and justified, and when there is a legal basis for processing the personal data. When collecting data on individuals for research purposes, all ethical and legal guidelines for the research in question must be followed. This must be borne in mind throughout the life cycle of the research, from design to implementation and post-research storage. As a general rule, application of the General Data Protection Regulation (GDPR), should always be considered, unless you are absolutely sure that there is no human-related information in the research data. 

Personal data is any information relating to an identified or identifiable natural person: 

• direct identifiers that alone are enough to identify a person (e.g. such as full name, social security number, email address containing the personal name, biometric identifiers such as facial image, voice patterns, fingerprints, iris, handwritten signature) 
• strong indirect identifiers with which a person can be identified with reasonable effort (e.g. postal address, phone number, IP-address, student number, insurance number, bank account number, exact annual income, vehicle registration number, unusual job title, rare disease) 
• indirect identifiers, i.e. information which on their own are not enough to identify someone but when linked with other available information, could be used for deducing the identity of the person (e.g. sex, age, principal abode, profession, workplace, education, school, dates such as date of birth, death, accident).
  

When processing personal data, it is important to identify information belonging to certain special category of personal data, known in everyday language as sensitive personal data. These include e.g. religious beliefs, health information, and trade union membership. It is also important to remember the difference between pseudonymous and anonymous research data, which are discussed in the next section (Processing personal data). 

In accordance with research ethical principles, the research subjects should be informed about what kind of data will be collected, how it is handled during the research and what will happen to the data after the research. It is therefore essential to plan the management of data during and after the research right from the outset. Research involving human subjects may also require prior ethical review. As a general rule, personal data should be minimized when collecting research data. This means that only such personal data necessary for the research purpose should be collected. 

Research organisations will instruct on the practices for handling personal data as required by the GDPR. For example, UEF Intranet provides guidelines and templates for the processing of personal data in scientific research (requires UEF credentials). The templates will help you in preparing the necessary documents. These might include: 

• privacy statement  
• participant information 
• ethical consent to participate 
• consent to process personal data 
• an assessment of data protection risks or data protection impact assessment (check your own organisation’s instructions, processes, and templates)
  

In data management, special attention must be paid to secure storing, processing, and transferring of data (see Chapter 4), so that only those who have justified reasons to access the data can access it. It is good to plan carefully how the research participants are informed about data processing and data management, for example, how date security is taken care of, how long the data is preserved, where will the data be shared for reuse and how.  

For research purposes data can be processed with identifiers, or as pseudonymous. Pseudonymization means removing or replacing identifiers with pseudonyms or codes, which are kept separately. Pseudonymized data is still personal data. 

Anonymization means removing all identifiers permanently. Several techniques and tools can be used in anonymisation.  Anonymisation is necessary if the data is shared openly or stored for reuse purposes. It should be remembered that anonymisation as a procedure involves the processing of personal data, but the anonymised research data no longer contain personal data. At this point, it can be treated as any other data that does not contain personal information. However, it is important to remember that it may not be possible to produce completely anonymised data. Read more about personal data and anonymization from FSD guidelines. 

For further information on processing personal data, see, e.g. Personal data processing in scientific research in UEF Intranet (requires UEF login). 

• Finnish advisory board on research integrity (TENK)
• UEF Research Ethics
• The Office of the Data Protection Ombudsman