Funding bodies often state something like this in their regulations:

Data should be openly accessible whenever legally, ethically and technically possible.

It is highly advisable to implement this guiding principle from the very beginning of your research project, as it can be difficult and complicated to obtain publication or exploitation rights afterwards.

Have you ever wondered how to license or protect your research data? What about reusing the research data of others? Perhaps certain rights are already protected. There are cases in which data itself is not protected unless it is part of a data set. Laws protecting confidential information or personal data may also help protect research data.

Please check in detail on a case-by-case basis whether and what rights may exist to your data record or that of another person. 

More information

If you want to publish data or make it openly available, ensuring you have the right to do so is important. Possible publication or reuse of your data should be coordinated with all research partners as soon as possible. You should also consider the possibility of embargo periods.

It may not be entirely clear who holds rights to the data, but it is essential. Only the rights owner is entitled to distribute licenses, even free licenses, for the use of data.

As a rule, the TU Wien holds the exploitation rights to the research results and data you produce as a TU Wien employee. To grant licences as a data producer, you must obtain the consent of the Head of the Research Unit or Head of the Institute. It is recommended to have this consent confirmed in writing.

Provided that your Head of the Research Unit or your Head of the Institute has agreed to the granting of licences, you, as a data producer, can grant licences in your own name in accordance with the TU Wien Research Data Management Policy. The right of data producers to grant licences continues to exist after leaving the TU Wien unless otherwise agreed upon between the data producer and the TU Wien.

Suppose your data set reaches the level of a work according to the Copyright Act (= original intellectual creation in the fields of literature, including scientific art, sound art, fine arts and film art). In that case, you also have the right to be named as the author in any case.

In the case of several authors of a work, all authors are, in principle, jointly entitled to make decisions and appoint a person authorised to represent them. When uploading data to a repository, all persons involved can generally be named as authors.

Software licences are the full responsibility of the individual Institutes.

In the case of contract research, the clients are usually the rights holders. Details can be found in the corresponding contracts, among other things. Please contact the TU Wien Contract Services experts on this topic.

The processing of personal data is carried out in strict compliance with the principles and requirements laid down in the GDPR (General Data Protection Regulation), the DSG (Data Protection Act) and the FOG (Research Organisation Act). Examination of the lawfulness of the processing must always be carried out on a case-by-case basis.

Further information on this topic can be found on the TU Wien Data Protection, opens an external URL in a new window website.

Please heed these policies, codes of conduct, and websites:

Please consider data-related ethical aspects of your research, for example, how data is stored and transferred, who has access to or can use it, and how long the data will be kept. Make sure you demonstrate awareness of these aspects and plan accordingly. There may be ethical reasons for restricting access to research data fully or partially. These reasons include, for example, avoiding certain risks: People (individuals, small groups, minorities), the environment, or society should not be at risk of harm.

The Service Unit Responsible Research Practices supports researchers and lecturers at TU Wien in questions regarding research ethics and research integrity. 

Please also see the TU Wien Code of Conduct – Rules to Ensure Good Scientific Practice, opens an external URL in a new window.

For more information contact Dr. Marjo Rauhala, head of the Service Unit of Responsible Research Practices.

Data is considered anonymised if it can no longer be assigned to a specific person. This contrasts with data that has merely been pseudonymised. In pseudonymisation, identifiable characteristics are replaced by code numbers or other keys, and the assignment process is documented so that the original information can still be restored - with varying degrees of effort. Pseudonymised data is, therefore, still subject to the GDPR. Whether data is to be classified as anonymised or pseudonymised is often not easy to answer, and it also depends on who receives the data and what possibilities this person has. Therefore, the combination of anonymisation/pseudonymisation is used below.

The following recommendations for anonymisation/pseudonymisation are taken from the Data Deposit Guideline of AUSSDA, opens an external URL in a new window, the Austrian Social Science Data Archive.

(In)direct identifiers 

Removal of all (in)direct identifiers: 
Delete variable
  • Social security number
  • ID number from 3rd party (data collection institute)
  • Full name
  • Email address
  • Phone numbers
  • Postal codes
  • Date of birth as (DDMMYYYY)
  • Workplace/employer
  • Vehicle registration number
  • Bank account number
  • IP address
  • Student ID number
  • Passport/identity card number

Open questions

Answers to open questions:
Thoroughly check or delete answers to open questions
  • Do you have any experience in working in politics? Answer: I have been elected as delegate in the parliament for party XYZ for 12 years in a row.
    --> Delete or recode!
  • Do you carry out any honorary duties? Answer: I have been working as a volunteer in the fire department in village XYZ in district ABC. 
    --> Delete or recode!

 

Standard demographic variables 

Age:
Summarize if margins fizzle out
  • Younger than 18 years
  • 19
  • 20
  • …
  • 70 and older

Occupational status:
Categorize into groups with min. 20 observations
  • Employed
  • Self-employed
  • Student/in training/in school
  • Retired
  • Unemployed
  • Other (e.g. military service, social service)
Field of education:
Categorize into groups with min. 20 observations
  • Building and civil engineering
  • Food processing

Years of schooling:
Summarize into groups with min. 20 observations and if margins fizzle out
  • Less than 5 years
  • 6
  • 7
  • 8
  • …
  • 13 and more
Income:
Categorize into broader categories and if margins fizzle out
  • Below 500 EUR
  • 500 to below 1000 EUR
  • 1000 to below 1500 EUR, etc.
  • More than 4000 EUR (e.g.)
Affiliation to religious groups:
Categorize into groups with min. 20 observations
  • Christian
  • Jewish
  • Islamic etc.
Membership in associations/clubs/political parties/trade union:
Categorize into groups with min. 20 observations
  • Recode “member of dachshund breeders association (in Linz)” to a broader category
Household composition:
Summarize into groups with min. 20 observations and if margins fizzle out
 
  • 1 person
  • 2 persons, etc.
  • > 5 persons
Nationality:
Categorize into groups with min. 20 observations or make broader categories
 
  • Austrian
  • Ethiopian
Mother tongue:
Categorize into groups with min. 20 observations
 
  • German
  • Kisuaheli
Need for social welfare:
Categorize into groups with min. 20 observations
 
  • Program A
  • Program B
Drug abuse:
Categorize into groups with min. 20 observations
  • Drug addiction: yes, no
Legal background information:
Delete if less than 20 observations in one category
  • Judicially condemned: yes, no
Health information:
Delete if less than 20 observations in one category
  • Suffer from depression: yes, no
Country of origin:
If less than 20 observations in one category, use standard for area codes used by the UN, UN M49 (if possible); use subregional category
 
  • Use UN geoscheme, e.g.
  • Eastern Africa, Middle Africa, Northern Africa, Southern Africa, Western Africa, etc.
ISCO variables on 3rd level
 
  • 3 digits level
NUTS variables on 2nd level
 
  • Provinces, Bundesländer

 

 

Source: Butzlaff, Iris (2022). Data Deposit Guideline (Public version) v2.0. Vienna: The Austrian Social Science Data Archive, opens an external URL in a new window

You can find more information on anonymisation in the AUSSDA Data Deposit Guidelines for qualitative data:
Kernecker, Theresa (2023). Data Deposit Guideline for qualitative data v1.0. Vienna: The Austrian Social Science Data Archive., opens an external URL in a new window