Update 23-research-data-commons.md

docs/_RDM-Reuse/23-research-data-commons.md

@@ -27,8 +27,8 @@ The **Cloud Layer** is the technical backbone based on a multi-cloud infrastruct
 
 The **Mediation Layer** provides a self-service collection of community-agnostic tools for creating FAIR data products. A community-specific community, for example, a team that works together in one of the Use Case projects of NFDI4Microbiota, is responsible for a specific data product and also responsible for its development process. A community understands the purpose the data product under its responsibility is generally used for, e.g. specific analyses and annotations. In particular, a data product uses common terminology of a certain discipline or subdiscipline. The mediation layer offers tools for data product developers to manage metadata, transform data from a technical data model into a semantic model, and improve data quality. Workflows serve to describe the steps of creating the data product from the source data sets, and thus, they also documents the provenance of a data product.
 
-The **Semantic Layer** provides the community-specific data products that are created and maintained in the Mediation Layer using the technical datasets accessible from the Cloud Layer and other data from interfaces to external data providers. The data products comply with the FAIR principles and are computer-actionable, i.e. a computer system is able to find and access them and understand the corresponding schema. Data products are either physically available or built on demand when the product is accessed. In addition, a self-service collection of community-agnostic tools are provided, such as Jupyter notebooks, which enable domain experts to create, deploy, and maintain community-specific applications on top of these data products.
+The **Semantic Layer** provides the community-specific data products that are created and maintained in the Mediation Layer using the technical data sets accessible from the Cloud Layer and other data from interfaces to external data providers. The data products comply with the FAIR principles and are computer-actionable, i.e. a computer system is able to find and access them and understand the corresponding schema. Data products are either physically available or built on demand when the product is accessed. In addition, a self-service collection of community-agnostic tools are provided, such as Jupyter notebooks, which enable domain experts to create, deploy, and maintain community-specific applications on top of these data products.
 
-The **Application Layer** consists of concrete applications and services developed for end users. These services can be community-agnostic, such as a search tool for datasets, or community-specific, such as a data portal for dragonflies or other species of interest. Community-specific applications are built on top of the data products in the semantic layer, while community-agnostic applications can access data from different layers. For example, the search tool requires access to data from the Cloud Layer and the Semantic Layer.
+The **Application Layer** consists of concrete applications and services developed for end users. These services can be community-agnostic, such as a search tool for data sets, or community-specific, such as a data portal for dragonflies or other species of interest. Community-specific applications are built on top of the data products in the semantic layer, while community-agnostic applications can access data from different layers. For example, the search tool requires access to data from the Cloud Layer and the Semantic Layer.
 
 In addition to these four layers, there are two other essential elements in the architecture. The first one **Management & Governance** features tools and policies to manage rules and access rights for the resources offered in the four horizontal layers, including user management and monitoring of usage of the technical resources. The second, called **External Data Interfaces**, features a collection of interfaces for accessing external data sets. Obviously, RDC requires connectivity to established large data providers without the need to manage copies of their data in the Cloud Layer.