Linking community and technology to enable FAIR data

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Linking community and technology to enable FAIR data

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 Use our Master Person Index

Due to privacy concerns and regulations regarding the storage of identifiable information, DataHub makes sure that all data is properly pseudonymised or anonymized before being stored in the DataHub infrastructure. To help you with the pseudonymisation of your study subject DataHub uses of a Master Person Index (MPI). The MPI is a versatile platform for person record linkage and management of personal information and personal identifiers originating from different data sources. This software replaces the study subject demographic information with a unique identifier. This identifier then allows us to aggregate data for this study subject without using identifying information.

Use-case specific matching algorithm

  1. As your dataset is unique you should setup a meeting with one of our data stewards.
  2. Together with the data steward you will analyze the structure of the dataset, identify the personal data fields and determine the proper matching algorithm.
  3. A data engineer at DataHub creates the so called pipeline.
  4. Together with the data steward you will test and approve the pipeline.
  5. Your data can now be automatically federated with other datasets.


By using our Master Person Index (MPI) it is possible to automatically link the subjects from your dataset to other datasets, provided the proper informed consent is in place, like the electronic health records or the Maastricht study. It is also possible to share your dataset with other researchers by giving them different keys per subject (we call this
double pseudonimization).

How to create use-case specific keys

  1. If another researcher requests access to your dataset you should first contact one of our data stewards.
  2. Together with the data steward you define the proper strategy (anonymization, pseudonymization, double pseudonimization, original key).
  3. Our Master Person Index creates the keys and places them in a copy of your dataset.

  4. Together with the data steward you will check and approve the new dataset.

  5. The other researcher can now be authorized to use the new dataset.

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