Gebruiker:DaanDuijn63/Kladblok

Data management

Data management, also known as Research Data Management (RDM), is the organization, documentation, storage, and preservation of the data resulting from the research process, where data can be broadly defined as the outcome of experiments or observations that validate research findings, and can take a variety of forms including numerical output (quantitative data), qualitative data, documentation, images, audio and video.

For researchers, adequately tracking and explaining how data is handled is of increasing relevance. With data management, better understood as a data management plan, the researcher shows how to think about the storage and protection of data and personal data. It is a document where the researcher writes down what he or she plans to do with the data throughout the research based on a number of questions.

Research data management plan (RDMP)[bewerken | brontekst bewerken]

Normally, a data management plan has a certain number of aspects that are addressed. However, such a plan is open to interpretation by the researcher and depends on the intended data. The extent to which personal data is handled is also a relevant aspect, as is the extent to which research should be anonymised. A typical RDMP has the following general characteristics in its framework:

- A brief summary of the research is given;

- An outline of relevant researchers and parties that are involved in the research project are given. In addition, the role these parties have in relation to data management is explained. A distinction can be made between involvement in the writing and adaptation of the DM, involvement in data collection and analysis, and involvement in data storage during the research process.

- Data collection: if existing data/personal data is used in the research project, this data should be described and the source should be mentioned.

- A check whether the project's data allow identification of a person. If working with personal data, all types of data that can be used for identification should be named. It should be indicated why the personal data are necessary to achieve the goals set to the research, or provide a clear argumentation for the choice made. It should also be indicated that no more personal data will be collected than is necessary for the purpose of the research.

- Next, the researcher will argue whether, and how, he or she will anonymise or pseudonymise data in order to protect the privacy of participants. In addition, the researcher will indicate whether the intended research requires an informed consent procedure. If so, the researcher can explain this procedure. Thereby, the privacy legislation (Wet AVG, art. lid 1) is an important addition to the procedure.

- Finally, the researcher can explain the storage and sharing of data during the research project. On this matter, the researcher substantiates how he or she uses secure data storage and back-up facilities.

Anonymization & pseudonymization[bewerken | brontekst bewerken]

Data anonymization is a type of information sanitization whose intent is privacy protection. It is the process of removing personally identifiable information from data sets, so that the people whom the data describe remain anonymous. There will always be a risk that anonymized data may not stay anonymous over time. Pairing the anonymized dataset with other data, clever techniques and raw power are some of the ways previously anonymous data sets have become de-anonymized; The data subjects are no longer anonymous. Researchers should elaborate how the data might not stay anonymous over time and explain how this development will be acted upon.

Pseudonymization is a data management and de-identification procedure by which personally identifiable information fields within a data record are replaced by one or more artificial identifiers, or pseudonyms. A single pseudonym for each replaced field or collection of replaced fields makes the data record less identifiable while remaining suitable for data analysis and data processing. A researcher should explain the form of anonymisation used in the research when working with personal data.