A brief overview of some terms relating to data and health

Data produced by people should be available to the people. Good healthcare, scientific research for better health, development of medication, health products and medical technologies, good practices, are based on the usage of shared data and knowledge. Health insurance is historically based on the fact that people put money in a box, a cash register, and people can take money from that cash register when they are ill. We have generalised this to our current health insurance, which is based on solidarity, people pay contributions and taxes, which can then be used by everyone when and where necessary. Actually, the same principles apply for data solidarity, meaning data produced by people should be available to the people. Just as all citizens contribute to the healthcare system through taxes, so too should data be shared for the common good. Data solidarity foregrounds the public value when it benefits people and communities without the risk of invading citizens’ direct privacy.

Data sovereignty is about the rules and systems that ensure data is stored, controlled, stored safely and used securely, and how it can be made easy to share and move between systems, while respecting key principles of digital independence. Data sovereignty is closely connected to the idea of digital self-determination, which means individuals have the right and ability to exercise autonomy over their digital presence, data and online activities. It also includes the idea of groups or communities having control over shared data.

A data space is like a shared environment or ecosystem where different organizations or people can safely share and use data. It’s built on rules and technologies that make sure the data is secure, easy to access, and used responsibly. The goal is to share data efficiently while keeping it safe and respecting privacy. In healthcare, a data space might let hospitals, researchers, and companies share patient data securely to improve treatments, without violating privacy rules.

Data standardization is the process of organizing data into a consistent format so it’s easier to understand, use, and share. It ensures that everyone who uses the data is on the same page, even if the data comes from different places or systems. Standardization makes data more reliable, compatible, and easier to analyze. If one system records "New York" as "NYC" and another as "New York City," standardizing them ensures all records are consistent, like always using "New York City." This helps avoid confusion, improves accuracy, and makes data integration smoother.

Data storage refers to how information is saved and kept for future use. Data storage is about finding a safe place to keep information, whether on your device, in the cloud, or on external hardware like a USB drive, or information stored in structured systems (databases) used by businesses for managing large amounts of data. It ensures the data is accessible, secure, and retrievable when required.

Data subjects are the people that share their data. A data subject is a person whose personal information (data) is being collected, stored, or processed. A data subject is the individual the data is about. They have rights over their data, such as knowing how it’s used, correcting it if it’s wrong, or asking for it to be deleted (depending on the law/regulation, like GDPR). When you shop online, you are the data subject for your order history, payment details, and shipping information.

Data transfer is the process of moving data from one place to another. This could mean sending data between devices, systems, or locations. Think of it like delivering a package—it’s about getting information from point A to point B. It’s how data travels over networks, like when you send an email, upload a file, or access a website. A cross-border transfer is also possible. It is transferring data between countries, often subject to laws and regulations to protect privacy and security.

A data user is a person, organization, or system that accesses and works with data. A data user is anyone who interacts with data for a specific purpose. They use the data to analyze, make decisions, or perform tasks. They could be reading, editing, analyzing, or sharing the data. A student using online research data for a project is a data user. Data users have responsibilities, like handling data responsibly and respecting privacy laws or guidelines.

DCAT-AP stands for Data Catalog Vocabulary Application Profile. It is developed and maintained by the European Commission for an Interoperable Europe. It is a standardized approach for describing public sector data sets, making it possible for data from diverse sources to be easily located, accessed and reused by various applications and stakeholders. It provides a common basis for standardized description of metadata and dataset within Europe to improve interoperability and make it easier to exchange data across borders and domains.

De-identification is the process of removing or masking personal information from a dataset so that individuals can no longer be easily identified. It’s a way to protect privacy while still allowing the data to be useful for analysis or sharing. It’s like blurring someone’s face in a photo—you can see the picture, but you can’t tell who the person is. A hospital might de-identify patient data by removing names and medical record numbers before sharing it with researchers.