In the emerging e-Science scenario users should be able to easily combine data resources and tools/services; and machines should automatically be able to trace paths and carry out interpretations. Users who want to participate need to move from a down-load first to a cyberinfrastructure paradigm, thus increasing their dependency on the seamless operation of all components in the Internet. Such a scenario is inherently complex and requires compliance to guidelines and standards to keep it working smoothly. Only a change in our culture of dealing with research data and awareness about the way we do data lifecycle management will lead to success. Since we have so many legacy resources that are not compliant with the required guidelines, since we need to admit obvious problems in particular with standardization in the area of semantics and since it will take much time to establish trust at the side of researchers, the e-Science scenario can only be achieved stepwise which will take much time.