Valle (Valle, 2012) has broken down the subject of scientific data management in ten areas. Among them the following can be named: Creation of logical collections which tries to abstract the physical data into logical collections, physical data handling in order to establish a mapping between the physical and the logical data views, persistence in order to define the data lifetime and deployment of mechanisms to counteract technology obsolescence, interoperability support to data location autonomy and putting various data collections together, security support for data access authorization and change verification, data ownership to define who Biotin-HPDP is responsible for data quality and meaning, metadata collection, management and access, knowledge and information discovery, data dissemination and publication.
Thus, there should be a shared and accepted understanding to bring all the cooperating parties onto the same page. To achieve electron acceptor common understanding and facilitate agreement, a way to capture the domain requirements including all major data generation and consumption functions is needed. The result needs to be understandable by its audience, i.e., scientists from the different communities involved, and needs to be sufficiently formal to avoid misunderstandings and differing interpretations. We believe that a conceptual model is the right vehicle to achieve this.
Thus, there should be a shared and accepted understanding to bring all the cooperating parties onto the same page. To achieve electron acceptor common understanding and facilitate agreement, a way to capture the domain requirements including all major data generation and consumption functions is needed. The result needs to be understandable by its audience, i.e., scientists from the different communities involved, and needs to be sufficiently formal to avoid misunderstandings and differing interpretations. We believe that a conceptual model is the right vehicle to achieve this.